Christine Alvarado outlines an approach for sketch understanding that builds and combines fragments of Bayesian Networks (BN). Fragment types are created up front that correspond to "shape and domain patterns." These are then instantiated based on evidence from the drawing strokes a user makes (e.g., several line segments may provide evidence for an arrow shape). Each fragment can be created multiple times for different hypotheses on the data. Their current work is very much in the prototype stage but it is eerily reminiscent of what we are hoping to do on a much larger scale in Hats.